A novel cuproptosis-related genes model in breast cancer prognosis

Abstract

Breast cancer (BRCA) is a highly heterogeneous malignancy with an urgent need to build a proper model to predict its prognosis. Cuproptosis is a recently discovered form of cell death, mediated by protein fatty acylation and tightly associated with mitochondrial metabolism. The role of cuproptosis-related genes (CRGs) in BRCA remains to be explored. We aimed to investigate the applications of CRGs in BRCA prognosis in different clinical contexts, including chemotherapy and immunotherapy, via bioinformatics analysis of the messenger RNA profiles and clinical data obtained from public databases. Molecular subtyping of CRGs was performed through consistent clustering analysis. Differentially expressed genes between different CRG clusters were identified. The differentially expressed genes were then used to build a risk assessment model using least absolute shrinkage and selection operator regression to predict patient survival with BRCA. The model was then validated with the data from the Molecular Taxonomy of Breast Cancer International Consortium, GSE96058, and GSE20685. Differences in somatic mutations, copy number variations, hallmark pathways, drug responses, and prognosis of immunotherapy and chemotherapy were analyzed by comparing the high-risk and low-risk groups. Patients with high-risk scores showed worse overall survival than those with low-risk scores. The results indicated significant differences between the 2 groups immune-related biological pathways and the variable immune status. It also suggests the differential sensitivity to chemotherapy between the 2 groups. The CRGs model showed the promise to predict the prognosis of BRCA patients and shed light on their treatment.